Abstract

How do children learn the causal structure of the environment? We first summarize a set of theories from the adult literature on causal learning, including associative models, parameter estimation theories, and causal structure learning accounts, as applicable to developmental science. We focus on causal graphical models as a description of children's causal knowledge, and the implications of this computational description for children's causal learning. We then examine the contributions of explanation and exploration to causal learning from a computational standpoint. Finally, we examine how children might learn causal knowledge from others and how computational and constructivist accounts of causal learning can be integrated. WIREs Cogn Sci 2014, 5:413–427. doi: 10.1002/wcs.1291 This article is categorized under: Psychology > Development and Aging Psychology > Learning